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10月17日周六
  1. Sam Altman38

    很高兴看到 Nick 的这篇分享! @nickcammarata:可解释性可能是当下世界上最重要的科学问题,因为如果我们不知道自己正在构建什么,就无法解决对齐问题。我想我对此有好消息。我曾在 OpenAI 参与机械可解释性的早期工作,当时得出的结论是悲观的,因为神经网络似乎会折叠成任意纠缠的结。多年后,我启动了 OpenAI 首个大规模自动化可解释性项目,用当时原始的工具发现约 2% 的神经元是可读的。 借助现代智能体工具和更细致的观察,情况看起来完全不同。在参数量高达数万亿的模型中,绝大多数神经元都是可读的,它们的回路很优美。许多著名的纠缠神经元案例其实是被误解了。我预想的障碍大多并不存在。我现在认为,雄心勃勃的可解释性研究会在我们最需要它的时候被解决,而且可能相当快,因为自动化科学家在不断进步。这对可解释性或许是一个苦涩的教训:简单的东西,在荒谬的规模下,是有效的。 https://already-clean.goodfire.com/

    引用Nick@nickcammarata

    interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://already-clean.goodfire.com/

  2. Google DeepMind78

    Google DeepMind 转发介绍,四个 Gemini 4 Argon 智能体在同一个 Minecraft 世界中从零开始,用 27 小时击败末影龙。它们只在沙盒中用代码和文本操作,共编写 2,783 个程序,通过聊天分配任务、共享笔记并互相调试错误,全程没有人类帮助。

    引用Maxence Faldor@maxencefaldor

    Four Gemini 4 Argon agents, one shared Minecraft world, one goal: defeat the Ender Dragon. 27 hours and 2,783 self-written programs later, the dragon fell. https://x.com/i/article/2111007444981645312

    推荐理由:四个 Gemini 4 Argon 智能体在共享沙盒中自行分工、写码与调试,展示了多智能体协作完成长程任务的一种路径。

10月16日周五
  1. Hugging Face78

    Reflection 宣布 Beam 已在多平台上线,以 Apache 2.0 协议开放权重,并与 40 多家生态伙伴合作。此次发布包含权重、tokenizer 和参考实现,BF16、FP8、NVFP4 权重及 DFlash2 drafter,以及训练 Beam 所用的开源工具 Mirror CLI。Hugging Face 转发该消息并预告 Reflection 团队直播讲解 Beam 的构建过程。

    引用Reflection@reflection_ai

    Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source harness we built and trained Beam on: Mirror CLI Weights and model card on Hugging Face: https://huggingface.co/reflection

    推荐理由:Reflection 公开 Beam 的权重、量化版本与训练工具 Mirror CLI,可据此了解其开放程度与复现路径。

  2. MiniMax (official)18

    明天将在 The Midway 与 @huggingface 一起拉开开源 AI 周的序幕 🪩 来舞池找 MiniMax 团队,我们准备了一些可爱的周边。 最新日程:https://opensourceaiweek.com @open_intel_fdn #OpenSourceAIWeek #OpenIntelligence

    引用Matt White@matthew_d_white

    Open Source AI Week started with a handful of events and a community bringing people together. Now in its third year, we have 65 events on the calendar—and we kick off tomorrow! Explore the lineup: https://opensourceaiweek.com/ Four highlights: 🎉 Tomorrow, Oct 16: Official Kickoff Party, hosted by Hugging Face https://luma.com/OpenTogether 🛠️ Saturday–Sunday, Oct 17–18: ExecuTorch Hackathon https://luma.com/executorch-hackathon 🎙️ Monday night, Oct 19: Open Weight Debates https://luma.com/592fcwx2 🏆 Next Friday, Oct 23: Open Source AI Awards Night, capping off the week https://luma.com/7ebvz0oq The Open Intelligence Foundation (@open_intel_fdn) is proud to be this year’s organizer, alongside a dedicated volunteer team and 32 backing organizations. Our commitment: • Official events will always be free and community-run. • We’ll work to bring the community together to plan diverse, high-value events at no cost, accessible to everyone. • We will never decline an event because it overlaps with one of ours. That goes against the spirit of open source and community. A shared calendar should create more opportunities to participate, collaborate, and learn. I’m proud to have been involved this year and grateful to everyone who made it happen. Thank you to our sponsors: @GoogleDeepMind, @Azure, @arcee_ai, @SemiAnalysis_, and @NEARProtocol Foundation. And our event hosts: @github, @fdotinc, @hackersquadio, @OSS4AI, @Qualcomm, @AIatMeta, @huggingface , @allen_ai, @MiniMax_AI, @UnslothAI, @PrimeIntellect, @NousResearch, @openclaw Foundation, @vllm_project, @lmsysorg, @inferact, @radixark and @anyscalecompute. Find the events that interest you and join us! https://opensourceaiweek.com/ #OpenSourceAIWeek #OpenIntelligence

  3. Mira Murati22

    今年最棒的部分之一,无论是在 Thinky 内部还是外部,就是看着人们追随自己的想法,一路走到他们自己拥有的权重之中。

    引用Thinking Machines@thinkymachines

    Tinker lets customers turn their expertise into cutting-edge models that they own. One year after launch, we look back at what we’ve shipped and what our users are building. https://thinkingmachines.ai/news/one-year-of-tinker/

  4. Anthropic40

    Anthropic 承诺提供 500 万美元资金、算力和 Claude 访问权限,帮助数学界应对 AI 带来的巨大变化。该资助与 Simons Foundation 合作,由后者以社区驱动方式管理,用于支持工作坊、会议和领域专家讲座。Simons Foundation 同时宣布投入 1500 万美元,并与 Anthropic、OpenAI 建立新合作。

    引用Simons Foundation@SimonsFdn

    How can the math community forge the best path forward in the AI age? We're committing $15M to workshops and initiatives — plus free AI computing resources for foundation-supported researchers — through new partnerships with @AnthropicAI and @OpenAI: https://bit.ly/4zpfIbW #math #science #AI

  5. Microsoft Research60

    微软研究院发布 Magnetar:基于标准哈希函数的通用零知识证明系统

    微软研究院发布零知识虚拟机 Magnetar,可直接证明用 Rust、Go 或 C 编写的程序执行过程,并输出可在约 12 毫秒内验证的简短证明。在 15 个基准程序上,它比同类最快的 zkVM 平均快 10 到 39 倍,在笔记本上每秒可证明 7.2 至 8.7 百万条 RISC-V 指令。

    推荐理由:微软研究院梳理十余年零知识证明研究脉络,说明 Magnetar 如何在通用性、速度与密码学假设之间同时取得进展。

10月15日周四
  1. NVIDIA73

    NVIDIA 发布 Nemotron 3.5 Super VL,扩充其 Nemotron 开源模型系列,将视觉理解、推理与工具调用结合,用于帮助 AI 智能体在更短时间内完成更多工作。该模型支持文本、图像、文档和视频理解,总参数 120B、激活参数 12B。NVIDIA 表示开源模型让用户能按自己的方式定制、部署和构建。

    引用NVIDIA AI@NVIDIAAI

    Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵

    推荐理由:官方给出新开源多模态模型的总参数与激活参数,读者可据此判断其本地部署与定制空间。

  2. OpenAI40

    OpenAI 向 Simons Foundation 新计划出资 500 万美元,用于帮助数学家检验、理解并在此基础上推进 AI 产出的结果,包括 OpenAI 近期发布的研究成果。

    引用Simons Foundation@SimonsFdn

    How can the math community forge the best path forward in the AI age? We're committing $15M to workshops and initiatives — plus free AI computing resources for foundation-supported researchers — through new partnerships with @AnthropicAI and @OpenAI: https://bit.ly/4zpfIbW #math #science #AI